Relevance Feedback is a technique used for enhancing retrieval accuracy in multimedia database systems. In this paper two novel relevance feedback algorithms are proposed for 3D object databases, in which the relative scores of various users, which express users’ subjectivity, are kept accumulatively as additional descriptors. Each object is interpreted as a charged particle, whose relative scores represent the value of the charge. Based on these charges, semantic forces are calculated between the 3D objects, which are repelled or attracted properly in the feature space. The forces are of dual nature, semantic and geometric, in the first algorithm, whereas they are purely semantic in the second one. Furthermore, a novel algorithm for annotation propagation is developed, which is based on a linear prediction scheme of the changes that must be made in the feature vector of a newly added 3Dobject in the database, according to alterations that has already taken place to objects in the database. The combination of low and high level features in one formula, is able to fill the semantic gap as much as possible till time being, while the proposed content-free retrieval method illustrates the fact that in the long run, a purely semantic algorithm can provide excellent retrieval results.